Robert Hsueh

A global analysis of cross-talk in a mammalian cellular signalling network

Cellular information processing requires the coordinated activity of a large network of intracellular signalling pathways. Cross-talk between pathways provides for complex non-linear responses to combinations of stimuli, but little is known about the density of these interactions in any specific cell. Here, we have analysed a large-scale survey of pathway interactions carried out by the Alliance for Cellular Signalling (AfCS) in RAW 264.7 macrophages. Twenty-two receptor-specific ligands were studied, both alone and in all pairwise combinations, for Ca2+ mobilization, cAMP synthesis, phosphorylation of many signalling proteins and for cytokine production. A large number of non-additive interactions are evident that are consistent with known mechanisms of cross-talk between pathways, but many novel interactions are also revealed. A global analysis of cross-talk suggests that many external stimuli converge on a relatively small number of interaction mechanisms to provide for context-dependent signalling.

Use of a cAMP BRET Sensor to Characterize a Novel Regulation of cAMP by the Sphingosine 1-Phosphate/G13 Pathway

Regulation of intracellular cyclic adenosine 3 ′,5 ′-monophosphate (cAMP) is integral in mediating cell growth, cell differentiation, and immune responses in hematopoietic cells. To facilitate studies of cAMP regulation we developed a BRET (bioluminescence resonance energy transfer) sensor for cAMP, CAMYEL (cAMP sensor using YFP-Epac-RLuc), which can quantitatively and rapidly monitor intracellular concentrations of cAMP in vivo. This sensor was used to characterize three distinct pathways for modulation of cAMP synthesis stimulated by presumed Gs-dependent receptors for isoproterenol and prostaglandin E2. Whereas two ligands, uridine 5 ′-diphosphate and complement C5a, appear to use known mechanisms for augmentation of cAMP via Gq/calcium and Gi, the action of sphingosine 1-phosphate (S1P) is novel. In these cells, S1P, a biologically active lysophospholipid, greatly enhances increases in intracellular cAMP triggered by the ligands for Gs-coupled receptors while having only a minimal effect by itself. The enhancement of cAMP by S1P is resistant to pertussis toxin and independent of intracellular calcium. Studies with RNAi and chemical perturbations demonstrate that the effect of S1P is mediated by the S1P2 receptor and the heterotrimeric G13 protein. Thus in these macrophage cells, all four major classes of G proteins can regulate intracellular cAMP.

Suppression of LPS-Induced TNF-α Production in Macrophages by cAMP Is Mediated by PKA-AKAP95-p105

Distinct anchoring proteins enable cAMP signaling to selectively modulate macrophage responses to pathogens. Specific Scaffolds Macrophages are innate immune cells that mediate early responses to infection by sensing microbial products through Toll-like receptors (TLRs) and producing proinflammatory compounds, such as tumor necrosis factor–α (TNF-α). A modulator of this proinflammatory response is prostaglandin E2 (PGE2), which activates G protein–coupled receptors that couple to Gαs, leading to the production of cyclic adenosine monophosphate (cAMP). As well as inhibiting the production of TNF-α by macrophages in response to the TLR4 agonist LPS, PGE2 and cAMP also stimulate the production of the anti-inflammatory cytokines interleukin-10 (IL-10) and granulocyte colony-stimulating factor (G-CSF) (see the Perspective by Peters-Golden). Wall et al. found that the pleiotropic effects of PGE2 and cAMP on LPS-stimulated cytokine production depended on the fate of cAMP-dependent protein kinase (PKA). Selective binding of activated PKA to different scaffold proteins known as A kinase–anchoring proteins (AKAPs) resulted in differential effects on the expression of genes encoding cytokines. In particular, cAMP-dependent inhibition of TNF-α expression involved phosphorylation of the NF-κB transcription factor p105 by PKA bound to AKAP95, which inhibited the nuclear translocation of the transcription factor, whereas the effect of PKA on the enhancement of G-CSF expression was mediated by another AKAP; the effect of PKA on IL-10 expression was AKAP-independent. Together, these data uncover crosstalk between TLR4 and cAMP signaling pathways that depend on the differential localization of PKA by different scaffold proteins, which could have implications for anti-inflammatory therapies. The activation of macrophages through Toll-like receptor (TLR) pathways leads to the production of a broad array of cytokines and mediators that coordinate the immune response. The inflammatory potential of this response can be reduced by compounds, such as prostaglandin E2, that induce the production of cyclic adenosine monophosphate (cAMP). Through experiments with cAMP analogs and multigene RNA interference (RNAi), we showed that key anti-inflammatory effects of cAMP were mediated specifically by cAMP-dependent protein kinase (PKA). Selective inhibitors of PKA anchoring, time-lapse microscopy, and RNAi screening suggested that differential mechanisms of PKA action existed. We showed a specific role for A kinase–anchoring protein 95 in suppressing the expression of the gene encoding tumor necrosis factor–α, which involved phosphorylation of p105 (also known as Nfkb1) by PKA at a site adjacent to the region targeted by inhibitor of nuclear factor κB kinases. These data suggest that crosstalk between the TLR4 and cAMP pathways in macrophages can be coordinated through PKA-dependent scaffolds that localize specific pools of the kinase to distinct substrates.

Overview of the Alliance for Cellular Signaling

The Alliance for Cellular Signaling is a large-scale collaboration designed to answer global questions about signalling networks. Pathways will be studied intensively in two cells — B lymphocytes (the cells of the immune system) and cardiac myocytes — to facilitate quantitative modelling. One goal is to catalyse complementary research in individual laboratories; to facilitate this, all alliance data are freely available for use by the entire research community.The Alliance for Cellular Signaling is a large-scale collaboration designed to answer global questions about signalling networks. Pathways will be studied intensively in two cells — B lymphocytes (the cells of the immune system) and cardiac myocytes — to facilitate quantitative modelling. One goal is to catalyse complementary research in individual laboratories; to facilitate this, all alliance data are freely available for use by the entire research community.

Tyrosine kinase activation in the decision between growth, differentiation, and death responses initiated from the B cell antigen receptor.

Immunoglobulin-containing receptors expressed on B lineage lymphocytes play critical roles in the development and function of the humoral arm of the immune system. The preB cell antigen receptor (preBCR) contains the immunoglobulin mu heavy chain (Ig mu) and signals to the preB cell that heavy chain rearrangement has been successful, a process termed heavy chain selection. The B cell antigen receptor (BCR) contains both Ig heavy and light chains and is expressed on immature and mature B cells before and after antigen encounter. Both receptor types from a complex with the Ig alpha and Ig beta proteins that link the predominantly extracellular Ig with intracellular signal transduction pathways. Signaling through the BCR induces different cellular responses depending on the nature of the signaling agent and the development stage of the target cell. These responses include clonal anergy and apoptotic deletion in immature B cells and survival, proliferation, and differentiation in mature B and preB cells. Several protein tyrosine kinases are activated rapidly following engagement of the BCR/preBCR complexes, including members of the Src family (Lyn and Blk), the Syk/ZAP70 family (Syk), and the Tec family (Btk). In this review, we discuss possible mechanisms by which engagement of these similar receptor complexes can give rise to different cellular responses and the role that these kinases play in this process.

Analysis of the Major Patterns of B Cell Gene Expression Changes in Response to Short-Term Stimulation with 33 Single Ligands

We examined the major patterns of changes in gene expression in mouse splenic B cells in response to stimulation with 33 single ligands for 0.5, 1, 2, and 4 h. We found that ligands known to directly induce or costimulate proliferation, namely, anti-IgM (anti-Ig), anti-CD40 (CD40L), LPS, and, to a lesser extent, IL-4 and CpG-oligodeoxynucleotide (CpG), induced significant expression changes in a large number of genes. The remaining 28 single ligands produced changes in relatively few genes, even though they elicited measurable elevations in intracellular Ca2+ and cAMP concentration and/or protein phosphorylation, including cytokines, chemokines, and other ligands that interact with G protein-coupled receptors. A detailed comparison of gene expression responses to anti-Ig, CD40L, LPS, IL-4, and CpG indicates that while many genes had similar temporal patterns of change in expression in response to these ligands, subsets of genes showed unique expression patterns in response to IL-4, anti-Ig, and CD40L.

Components of the antigen processing and presentation pathway revealed by gene expression microarray analysis following B cell antigen receptor (BCR) stimulation

BackgroundActivation of naïve B lymphocytes by extracellular ligands, e.g. antigen, lipopolysaccharide (LPS) and CD40 ligand, induces a combination of common and ligand-specific phenotypic changes through complex signal transduction pathways. For example, although all three of these ligands induce proliferation, only stimulation through the B cell antigen receptor (BCR) induces apoptosis in resting splenic B cells. In order to define the common and unique biological responses to ligand stimulation, we compared the gene expression changes induced in normal primary B cells by a panel of ligands using cDNA microarrays and a statistical approach, CLASSIFI (Cl uster Assi gnment f or Biological I nference), which identifies significant co-clustering of genes with similar Gene Ontology™ annotation.ResultsCLASSIFI analysis revealed an overrepresentation of genes involved in ion and vesicle transport, including multiple components of the proton pump, in the BCR-specific gene cluster, suggesting that activation of antigen processing and presentation pathways is a major biological response to antigen receptor stimulation. Proton pump components that were not included in the initial microarray data set were also upregulated in response to BCR stimulation in follow up experiments. MHC Class II expression was found to be maintained specifically in response to BCR stimulation. Furthermore, ligand-specific internalization of the BCR, a first step in B cell antigen processing and presentation, was demonstrated.ConclusionThese observations provide experimental validation of the computational approach implemented in CLASSIFI, demonstrating that CLASSIFI-based gene expression cluster analysis is an effective data mining tool to identify biological processes that correlate with the experimental conditional variables. Furthermore, this analysis has identified at least thirty-eight candidate components of the B cell antigen processing and presentation pathway and sets the stage for future studies focused on a better understanding of the components involved in and unique to B cell antigen processing and presentation.

Deciphering signaling outcomes from a system of complex networks.

Despite being exposed to multiple ligands, a macrophage cell line exhibits a surprisingly limited number of responses. Complex Crosstalk Traditionally, signaling pathways are investigated by measuring the responses of cells to a single factor. Although this approach provides valuable information on the signaling pathways stimulated by that ligand in vitro, the situation in vivo is often much more complex. In the immune system, for example, macrophages are simultaneously exposed to many different cytokines during inflammation. Thus, signaling responses in such contexts cannot reliably be predicted on the basis of single-ligand studies. Hsueh et al. extended a previous analysis of the responses of a macrophage cell line to pairwise combinations of ligands by measuring the cytokines secreted in response to three-, four-, and five-way combinations of ligands and then mathematically analyzing the differences between the expected outcomes (based on lower-order combinations) and the measured outcomes. They found that despite the high number of possible interactions among the stimulated signaling pathways, a modest number of responses were observed. In addition, they also uncovered some unpredicted signaling outcomes, which should help in the future modeling of responses of cells in different contexts. Cellular signal transduction machinery integrates information from multiple inputs to actuate discrete cellular behaviors. Interaction complexity exists when an input modulates the output behavior that results from other inputs. To address whether this machinery is iteratively complex—that is, whether increasing numbers of inputs produce exponential increases in discrete cellular behaviors—we examined the modulated secretion of six cytokines from macrophages in response to up to five-way combinations of an agonist of Toll-like receptor 4, three cytokines, and conditions that activated the cyclic adenosine monophosphate pathway. Although all of the selected ligands showed synergy in paired combinations, few examples of nonadditive outputs were found in response to higher-order combinations. This suggests that most potential interactions are not realized and that unique cellular responses are limited to discrete subsets of ligands and pathways that enhance specific cellular functions.

Unravelling the signal-transduction network in B lymphocytes

The Alliance for Cellular Signaling has chosen the mouse B lymphocyte as a model system to understand basic principles that govern cellular signalling. Progress to that end has focused initially on establishing a reproducible experimental cell system and characterizing essential signalling responses. Although unravelling this complex network will take years, findings revealed in the interim will prove immensely useful to the scientific community at large.

Dual ligand stimulation of RAW 264.7 cells uncovers feedback mechanisms that regulate TLR-mediated gene expression

To characterize how signaling by TLR ligands can be modulated by non-TLR ligands, murine RAW 264.7 cells were treated with LPS, IFN-γ, 2-methyl-thio-ATP (2MA), PGE2, and isoproterenol (ISO). Ligands were applied individually and in combination with LPS, for 1, 2, and 4 h, and transcriptional changes were measured using customized oligo arrays. We used nonadditive transcriptional responses to dual ligands (responses that were reproducibly greater or less than the expected additive responses) as a measure of pathway interaction. Our analysis suggests that cross-talk is limited; <24% of the features with significant responses to the single ligands responded nonadditively to a dual ligand pair. PGE2 and ISO mainly attenuated, while 2MA enhanced, LPS-induced transcriptional changes. IFN-γ and LPS cross-regulated the transcriptional response induced by each other: while LPS preferentially enhanced IFN-γ-induced changes in gene expression at 1 h, IFN-γ signaling primarily attenuated LPS-induced changes at 4 h. Our data suggest specific cross-talk mechanisms: 1) LPS enhances the expression of IFN-γ- response genes by augmenting STAT1 activity and by activating NF-κB, which synergizes with IFN-γ-induced transcriptional factors; 2) IFN-γ attenuates the late LPS transcriptional response by increasing the expression of suppressor of cytokine signaling 1 and cytokine-inducible SH2-containing protein expression; 3) 2MA modulates LPS secondary transcriptional response by increasing IFN-β and inhibiting IL-10 gene expression; 4) PGE2 and ISO similarly regulate the LPS transcriptional response. They increase IL-10 transcription, resulting in attenuated expression of known IL-10-suppressed genes.